pCTR/pCVR prediction As is shown in many previous works, CTR/CVR prediction has been well studied in sponsored search advertising [1]}, [2]}, [3]}, [4]}, contextual advertising [5]}, [6]}, display advertising [7]}, [8]}, [9]}, and native advertising [10]}, [11]}. A common objective of these models is to accurately predict the probability of an ad being clicked once it is shown to end users. In our work, we incorporate history CTR/CVR observations with advertiser attributes (such as targeting location, device information, category and textual information, bid price, etc) for better prediction of CTR/CVRs. We build a convolution neural network model [12]} for CTR prediction using robust feature learningFor cold-start ad without any history information, we adopt the exploration-exploit approach [13]} to address the sparsity issue in conversion rate (e.g., 1% conversions in 5% of clicks) prediction..
